Unmanned vehicle recognition and threat management
AssigneeDigital Global Systems, Inc.
InventorsDavid William Kleinbeck, Armando Montalvo
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Abstract
Systems and methods for automated unmanned aerial vehicle recognition. A multiplicity of receivers captures RF data and transmits the RF data to at least one node device. The at least one node device comprises a signal processing engine, a detection engine, a classification engine, and a direction finding engine. The at least one node device is configured with an artificial intelligence algorithm. The detection engine and classification engine are trained to detect and classify signals from unmanned vehicles and their controllers based on processed data from the signal processing engine. The direction finding engine is operable to provide lines of bearing for detected unmanned vehicles.
Claims20
A system for signal identification in a radiofrequency (RF) environment, comprising:
- at least one node device including a processor and at least one memory in communication with at least one RF receiver;
- wherein the at least one RF receiver is operable to capture RF data in the RF environment and transmit the RF data to the at least one node device;
- wherein the at least one node device is operable to average Fast Fourier Transform (FFT) data derived from the RF data into at least one tile;
- wherein the at least one tile is visually represented as at least one waterfall image;
- wherein the at least one node device is operable to analyze the at least one waterfall image using machine learning (ML) or at least one convolutional neural network (CNN) to identify at least one signal, at least one signal type, and/or noise to create at least one analyzed waterfall image;
- and
- wherein the at least one analyzed waterfall image includes a visual indication of the at least one signal, the at least one signal type, and/or the noise.
An apparatus for signal identification in a radiofrequency (RF) environment, comprising:
- a node device including a processor and at least one memory;
- wherein the node device is operable to receive RF data from at least one RF receiver;
- wherein the node device is operable to average Fast Fourier Transform (FFT) data derived from the RF data into at least one tile;
- wherein the at least one tile is represented as at least one waterfall image;
- wherein the node device waterfall image is analyzed using machine learning (ML) or at least one convolutional neural network (CNN) to identify at least one signal, at least one signal type, and/or noise to create at least one analyzed waterfall image;
- and
- wherein the at least one analyzed waterfall image includes a visual indication of the at least one signal, the at least one signal type, and/or the noise.
A method for signal analysis in a radiofrequency (RF) environment, comprising:
- at least one RF receiver capturing RF data in the RF environment, converting the RF data to Fast Fourier Transform (FFT) data, and transmitting the FFT data to at least one node device;
- the at least one node device averaging the FFT data derived from the RF data into at least one tile, wherein the at least one tile is represented as at least one waterfall image;
- the at least one node device analyzing the at least one waterfall image using machine learning (ML) or at least one convolutional neural network (CNN) to identify at least one signal, at least one signal type, and/or noise;
- and
- the at least one node device creating at least one analyzed waterfall image based on the identification of the at least one signal, the at least one signal type, and/or the noise;
- wherein the at least one analyzed waterfall image includes a visual indication of the at least one signal, the at least one signal type, and/or the noise.
Dependencies are inferred from the claim text.